Personalized magnetoencephalography signal generation and enhancement for brain-computer interfaces

By integrating paired EEG-MEG data with prior knowledge of electromagnetic neurodynamics, a scenario-adaptive MEG signal generation model was constructed. This solved the problem of personalized generation across task scenarios and subjects, achieving high-precision MEG signal generation and enhancement, and improving the performance of the BCI system.

CN122195268BActive Publication Date: 2026-07-24SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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Patent Information

Application Number
CN202610670930.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-05-15
Publication Date
2026-07-24
Estimated Expiration
2046-05-15

AI Technical Summary

Technical Problem

Existing methods for generating MEG signals lack the ability to adapt to different tasks and scenarios and to generate personalized signals for different subjects. Furthermore, the theoretical modeling is detached from physical constraints, resulting in insufficient accuracy and adaptability of the generated MEG signals in practical applications.

Method used

By integrating paired EEG-MEG data with prior knowledge of electromagnetic neurodynamics through end-to-end joint training, a basic model of EEG-MEG representation is constructed. Combined with scene-adaptive fine-tuning and multidimensional neurophysiological fingerprinting, personalized MEG signals are generated.

Benefits of technology

It achieves high-precision signal generation across mission scenarios and subjects, enhances the spatial resolution and noise resistance of MEG signals, and improves the decoding accuracy and adaptability of the BCI system.

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Abstract

The application belongs to the technical field of non-invasive brain-computer interface, and particularly relates to a personalized magnetoencephalogram signal generation and enhancement method for brain-computer interface. The method comprises the following steps: constructing an electroencephalogram-magnetoencephalogram representation basic model by fusing electroencephalogram-magnetoencephalogram paired data and electromagnetic neurodynamic priori knowledge through end-to-end joint training; performing scene adaptive fine-tuning for a target brain-computer interface task based on the electroencephalogram-magnetoencephalogram representation basic model to obtain a scene model corresponding to the target brain-computer interface task; and generating magnetoencephalogram signals of a target subject based on the scene model and fusing multi-dimensional neurophysiological fingerprints of the target subject as priori guidance.
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